Pricing & Revenue Data Scientist

Senior Data Scientist - Optimisation (Contract)
Outside IR35 | £400-450 per day | 3-month initial term | Hybrid London (2-3 days on-site)

The brief
A global marketing-data organisation is upgrading the engine that matches millions of survey invitations to the right respondents. Your task: treat the matching pipeline as a full-scale optimisation problem and raise both accuracy and yield.

Core responsibilities
Model optimisation

- refactor and improve existing matching/segmentation models; design objective functions that balance cost, speed and data quality.

Experimentation

- set up offline metrics and online A/B tests; analyse uplift and iterate quickly.

Production delivery

- build scalable pipelines in AWS SageMaker (moving to Azure ML); containerise code and hook into CI/CD.

Monitoring & tuning

- track drift, response quality and spend; implement automated retraining triggers.

Collaboration

- work with Data Engineering, Product and Ops teams to translate business constraints into mathematical formulations.

A typical day
Morning stand-up: align on performance targets and new constraints.

Data dive: explore panel behaviour in Python/SQL, craft new features.

Modelling sprint: run hyper-parameter sweeps or explore heuristic/greedy and MIP/SAT approaches.

Deployment: ship a model as a container, update an Airflow (or Azure Data Factory) job.

Review: inspect dashboards, compare control vs. treatment, plan next experiment.

Tech stack
Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow)
SQL (Redshift, Snowflake or similar)
AWS SageMaker → Azure ML migration, with Docker, Git, Terraform, Airflow / ADF
Optional extras: Spark, Databricks, Kubernetes.

What you'll bring
3-5+ years building optimisation or recommendation systems at scale.

Strong grasp of mathematical optimisation (e.g., linear/integer programming, meta-heuristics) as well as ML.

Hands-on cloud ML experience (AWS or Azure).

Proven track record turning prototypes into reliable production services.

Clear communication and documentation habits.

Desired Skills and Experience
Experience & skills checklist
3-5 + yrs optimisation/recommender work at production scale (dynamic pricing, yield, marketplace matching).
Mathematical optimisation know-how - LP/MIP, heuristics, constraint tuning, objective-function design.
Python toolbox: pandas, NumPy, scikit-learn, PyTorch/TensorFlow; clean, tested code.
Cloud ML: hands-on with AWS SageMaker plus exposure to Azure ML; Docker, Git, CI/CD, Terraform.
SQL mastery for heavy-duty data wrangling and feature engineering.
Experimentation chops - offline metrics, online A/B test design, uplift analysis.
Production mindset: containerise models, deploy via Airflow/ADF, monitor drift, automate retraining.
Soft skills: clear comms, concise docs, and a collaborative approach with DS, Eng & Product.
Bonus extras: Spark/Databricks, Kubernetes, big-data panel or ad-tech experience.